Automatic Verb Classification Using Distributions of Grammatical Features

We apply machine learning techniques to classify automatically a set of verbs into lexical semantic classes, based on distributional approximations of diatheses, extracted from a very large annotated corpus. Distributions of four grammatical features are sufficient to reduce error rate by 50% over chance. We conclude that corpus data is a usable repository of verb class information, and that corpus-driven extraction of grammatical features is a promising methodology for automatic lexical acquisition. 1 Introduction Recent years have witnessed a shift in grammar development methodology, from crafting large grammars, to annotation of corpora. Correspondingly, there has been a change from developing rule-based parsers to developing statistical methods for inducing grammatical knowledge from annotated corpus data. The shift has mostly occurred because building wide-coverage grammars is time-consuming, error prone, and difficult. The same can be said for crafting the rich lexi..